model_creators:
- Jordan Painter, Diptesh Kanojia
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
widget:
- text: I'm ecstatic my flight was just delayed
model-index:
- name: bertweet-base-finetuned-SARC-DS
results: []
Utilising Weak Supervision to Create S3D: A Sarcasm Annotated Dataset
This is the repository for the S3D dataset published at EMNLP 2022. The dataset can help build sarcasm detection models.
bertweet-base-finetuned-SARC-DS
This model is a fine-tuned version of vinai/bertweet-base on the SARC dataset. It achieves the following results on the evaluation set:
- Loss: 1.7094
- Accuracy: 0.7636
- Precision: 0.7637
- Recall: 0.7636
- F1: 0.7636
Model description
The given description for BERTweet by VinAI is as follows:
BERTweet is the first public large-scale language model pre-trained for English Tweets. BERTweet is trained based on the RoBERTa pre-training procedure. The corpus used to pre-train BERTweet consists of 850M English Tweets (16B word tokens ~ 80GB), containing 845M Tweets streamed from 01/2012 to 08/2019 and 5M Tweets related to the COVID-19 pandemic.
Training and evaluation data
This vinai/bertweet-base model was finetuned on the SARC dataset. The dataset is intended to help build sarcasm detection models.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 43
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.4978 | 1.0 | 44221 | 0.4899 | 0.7777 | 0.7787 | 0.7778 | 0.7775 |
| 0.4413 | 2.0 | 88442 | 0.4833 | 0.7798 | 0.7803 | 0.7798 | 0.7797 |
| 0.3943 | 3.0 | 132663 | 0.5387 | 0.7784 | 0.7784 | 0.7784 | 0.7784 |
| 0.3461 | 4.01 | 176884 | 0.6184 | 0.7690 | 0.7701 | 0.7690 | 0.7688 |
| 0.3024 | 5.01 | 221105 | 0.6899 | 0.7684 | 0.7691 | 0.7684 | 0.7682 |
| 0.2653 | 6.01 | 265326 | 0.7805 | 0.7654 | 0.7660 | 0.7654 | 0.7653 |
| 0.2368 | 7.01 | 309547 | 0.9066 | 0.7643 | 0.7648 | 0.7643 | 0.7642 |
| 0.2166 | 8.01 | 353768 | 1.0548 | 0.7612 | 0.7620 | 0.7611 | 0.7610 |
| 0.2005 | 9.01 | 397989 | 1.0649 | 0.7639 | 0.7639 | 0.7639 | 0.7639 |
| 0.1837 | 10.02 | 442210 | 1.1805 | 0.7621 | 0.7624 | 0.7621 | 0.7621 |
| 0.1667 | 11.02 | 486431 | 1.3017 | 0.7658 | 0.7659 | 0.7659 | 0.7658 |
| 0.1531 | 12.02 | 530652 | 1.2947 | 0.7627 | 0.7628 | 0.7627 | 0.7627 |
| 0.1377 | 13.02 | 574873 | 1.3877 | 0.7639 | 0.7639 | 0.7639 | 0.7639 |
| 0.1249 | 14.02 | 619094 | 1.4468 | 0.7613 | 0.7616 | 0.7613 | 0.7612 |
| 0.1129 | 15.02 | 663315 | 1.4951 | 0.7620 | 0.7621 | 0.7620 | 0.7620 |
| 0.103 | 16.02 | 707536 | 1.5599 | 0.7619 | 0.7624 | 0.7619 | 0.7618 |
| 0.0937 | 17.03 | 751757 | 1.6270 | 0.7615 | 0.7616 | 0.7615 | 0.7615 |
| 0.0864 | 18.03 | 795978 | 1.6918 | 0.7622 | 0.7624 | 0.7622 | 0.7621 |
| 0.0796 | 19.03 | 840199 | 1.7094 | 0.7636 | 0.7637 | 0.7636 | 0.7636 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.10.1+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1